DeepAI
Log In Sign Up

Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories

07/03/2019
by   Jacopo Tagliabue, et al.
0

Knowing if a user is a buyer or window shopper solely based on clickstream data is of crucial importance for e-commerce platforms seeking to implement real-time accurate NBA (next best action) policies. However, due to the low frequency of conversion events and the noisiness of browsing data, classifying user sessions is very challenging. In this paper, we address the clickstream classification problem in the eCommerce industry and present three major contributions to the burgeoning field of AI-for-retail: first, we collected, normalized and prepared a novel dataset of live shopping sessions from a major European e-commerce website; second, we use the dataset to test in a controlled environment strong baselines and SOTA models from the literature; finally, we propose a new discriminative neural model that outperforms neural architectures recently proposed at Rakuten labs.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/30/2018

A Narrative Literature Review and E-Commerce Website Research

In this study, a narrative literature review regarding culture and e-com...
10/25/2019

Data Preprocessing for Evaluation of Recommendation Models in E-Commerce

E-commerce businesses employ recommender models to assist in identifying...
05/31/2019

A multi-series framework for demand forecasts in E-commerce

Sales forecasts are crucial for the E-commerce business. State-of-the-ar...
08/15/2019

When Your Friends Become Sellers: An Empirical Study of Social Commerce Site Beidian

Past few years have witnessed the emergence and phenomenal success of st...
11/01/2019

The Consistency of Trust-Sales Relationship in Latin-American E-commerce

A taken-for-granted factor that facilitates the economic transactions in...